Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Sunday 28 June 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 157,722 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 2[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bed… 27 mk454hr East of E…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bla… 9 bb12fd North West
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bro… 11 br33ql London
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_can… 9 ws111jp Midlands
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_cit… 12 n15lz London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_enf… 7 en40dy London
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ham… 6 dl62uu North Eas…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_har… 24 ts232la North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_kin… 6 kt11eu London
## [90m# … with 157,712 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
## 88 2020-05-27 East of England 12
## 89 2020-05-28 East of England 17
## 90 2020-05-29 East of England 16
## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
## 95 2020-06-03 East of England 10
## 96 2020-06-04 East of England 7
## 97 2020-06-05 East of England 12
## 98 2020-06-06 East of England 5
## 99 2020-06-07 East of England 9
## 100 2020-06-08 East of England 5
## 101 2020-06-09 East of England 6
## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 0
## 104 2020-06-12 East of England 9
## 105 2020-06-13 East of England 5
## 106 2020-06-14 East of England 4
## 107 2020-06-15 East of England 7
## 108 2020-06-16 East of England 3
## 109 2020-06-17 East of England 7
## 110 2020-06-18 East of England 4
## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 2
## 113 2020-06-21 East of England 3
## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 4
## 116 2020-06-24 East of England 3
## 117 2020-06-25 East of England 0
## 118 2020-06-26 East of England 3
## 119 2020-03-01 London 0
## 120 2020-03-02 London 0
## 121 2020-03-03 London 0
## 122 2020-03-04 London 0
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## 125 2020-03-07 London 0
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## 137 2020-03-19 London 25
## 138 2020-03-20 London 44
## 139 2020-03-21 London 49
## 140 2020-03-22 London 54
## 141 2020-03-23 London 63
## 142 2020-03-24 London 87
## 143 2020-03-25 London 113
## 144 2020-03-26 London 129
## 145 2020-03-27 London 130
## 146 2020-03-28 London 122
## 147 2020-03-29 London 146
## 148 2020-03-30 London 149
## 149 2020-03-31 London 181
## 150 2020-04-01 London 202
## 151 2020-04-02 London 191
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## 163 2020-04-14 London 144
## 164 2020-04-15 London 142
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## 166 2020-04-17 London 100
## 167 2020-04-18 London 101
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## 171 2020-04-22 London 109
## 172 2020-04-23 London 77
## 173 2020-04-24 London 71
## 174 2020-04-25 London 58
## 175 2020-04-26 London 53
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## 178 2020-04-29 London 44
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## 212 2020-06-02 London 7
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## 215 2020-06-05 London 4
## 216 2020-06-06 London 0
## 217 2020-06-07 London 4
## 218 2020-06-08 London 5
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## 220 2020-06-10 London 7
## 221 2020-06-11 London 5
## 222 2020-06-12 London 3
## 223 2020-06-13 London 3
## 224 2020-06-14 London 2
## 225 2020-06-15 London 1
## 226 2020-06-16 London 2
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## 231 2020-06-21 London 4
## 232 2020-06-22 London 2
## 233 2020-06-23 London 0
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## 235 2020-06-25 London 2
## 236 2020-06-26 London 0
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## 238 2020-03-02 Midlands 0
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## 243 2020-03-07 Midlands 0
## 244 2020-03-08 Midlands 3
## 245 2020-03-09 Midlands 1
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## 247 2020-03-11 Midlands 2
## 248 2020-03-12 Midlands 6
## 249 2020-03-13 Midlands 5
## 250 2020-03-14 Midlands 4
## 251 2020-03-15 Midlands 5
## 252 2020-03-16 Midlands 11
## 253 2020-03-17 Midlands 8
## 254 2020-03-18 Midlands 13
## 255 2020-03-19 Midlands 8
## 256 2020-03-20 Midlands 28
## 257 2020-03-21 Midlands 13
## 258 2020-03-22 Midlands 31
## 259 2020-03-23 Midlands 33
## 260 2020-03-24 Midlands 41
## 261 2020-03-25 Midlands 48
## 262 2020-03-26 Midlands 64
## 263 2020-03-27 Midlands 72
## 264 2020-03-28 Midlands 89
## 265 2020-03-29 Midlands 92
## 266 2020-03-30 Midlands 90
## 267 2020-03-31 Midlands 123
## 268 2020-04-01 Midlands 140
## 269 2020-04-02 Midlands 142
## 270 2020-04-03 Midlands 124
## 271 2020-04-04 Midlands 151
## 272 2020-04-05 Midlands 164
## 273 2020-04-06 Midlands 140
## 274 2020-04-07 Midlands 123
## 275 2020-04-08 Midlands 186
## 276 2020-04-09 Midlands 139
## 277 2020-04-10 Midlands 127
## 278 2020-04-11 Midlands 142
## 279 2020-04-12 Midlands 139
## 280 2020-04-13 Midlands 120
## 281 2020-04-14 Midlands 116
## 282 2020-04-15 Midlands 147
## 283 2020-04-16 Midlands 102
## 284 2020-04-17 Midlands 118
## 285 2020-04-18 Midlands 115
## 286 2020-04-19 Midlands 92
## 287 2020-04-20 Midlands 107
## 288 2020-04-21 Midlands 86
## 289 2020-04-22 Midlands 78
## 290 2020-04-23 Midlands 103
## 291 2020-04-24 Midlands 79
## 292 2020-04-25 Midlands 72
## 293 2020-04-26 Midlands 81
## 294 2020-04-27 Midlands 74
## 295 2020-04-28 Midlands 68
## 296 2020-04-29 Midlands 53
## 297 2020-04-30 Midlands 56
## 298 2020-05-01 Midlands 64
## 299 2020-05-02 Midlands 51
## 300 2020-05-03 Midlands 52
## 301 2020-05-04 Midlands 61
## 302 2020-05-05 Midlands 59
## 303 2020-05-06 Midlands 59
## 304 2020-05-07 Midlands 48
## 305 2020-05-08 Midlands 34
## 306 2020-05-09 Midlands 37
## 307 2020-05-10 Midlands 42
## 308 2020-05-11 Midlands 33
## 309 2020-05-12 Midlands 45
## 310 2020-05-13 Midlands 40
## 311 2020-05-14 Midlands 37
## 312 2020-05-15 Midlands 40
## 313 2020-05-16 Midlands 34
## 314 2020-05-17 Midlands 31
## 315 2020-05-18 Midlands 34
## 316 2020-05-19 Midlands 34
## 317 2020-05-20 Midlands 36
## 318 2020-05-21 Midlands 32
## 319 2020-05-22 Midlands 27
## 320 2020-05-23 Midlands 34
## 321 2020-05-24 Midlands 19
## 322 2020-05-25 Midlands 26
## 323 2020-05-26 Midlands 33
## 324 2020-05-27 Midlands 29
## 325 2020-05-28 Midlands 28
## 326 2020-05-29 Midlands 20
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## 328 2020-05-31 Midlands 22
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## 330 2020-06-02 Midlands 22
## 331 2020-06-03 Midlands 24
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## 333 2020-06-05 Midlands 21
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## 335 2020-06-07 Midlands 17
## 336 2020-06-08 Midlands 16
## 337 2020-06-09 Midlands 18
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## 342 2020-06-14 Midlands 17
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## 346 2020-06-18 Midlands 14
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## 349 2020-06-21 Midlands 12
## 350 2020-06-22 Midlands 12
## 351 2020-06-23 Midlands 12
## 352 2020-06-24 Midlands 13
## 353 2020-06-25 Midlands 12
## 354 2020-06-26 Midlands 1
## 355 2020-03-01 North East and Yorkshire 0
## 356 2020-03-02 North East and Yorkshire 0
## 357 2020-03-03 North East and Yorkshire 0
## 358 2020-03-04 North East and Yorkshire 0
## 359 2020-03-05 North East and Yorkshire 0
## 360 2020-03-06 North East and Yorkshire 0
## 361 2020-03-07 North East and Yorkshire 0
## 362 2020-03-08 North East and Yorkshire 0
## 363 2020-03-09 North East and Yorkshire 0
## 364 2020-03-10 North East and Yorkshire 0
## 365 2020-03-11 North East and Yorkshire 0
## 366 2020-03-12 North East and Yorkshire 0
## 367 2020-03-13 North East and Yorkshire 0
## 368 2020-03-14 North East and Yorkshire 0
## 369 2020-03-15 North East and Yorkshire 2
## 370 2020-03-16 North East and Yorkshire 3
## 371 2020-03-17 North East and Yorkshire 1
## 372 2020-03-18 North East and Yorkshire 2
## 373 2020-03-19 North East and Yorkshire 6
## 374 2020-03-20 North East and Yorkshire 5
## 375 2020-03-21 North East and Yorkshire 6
## 376 2020-03-22 North East and Yorkshire 7
## 377 2020-03-23 North East and Yorkshire 9
## 378 2020-03-24 North East and Yorkshire 8
## 379 2020-03-25 North East and Yorkshire 18
## 380 2020-03-26 North East and Yorkshire 21
## 381 2020-03-27 North East and Yorkshire 28
## 382 2020-03-28 North East and Yorkshire 35
## 383 2020-03-29 North East and Yorkshire 38
## 384 2020-03-30 North East and Yorkshire 64
## 385 2020-03-31 North East and Yorkshire 60
## 386 2020-04-01 North East and Yorkshire 67
## 387 2020-04-02 North East and Yorkshire 74
## 388 2020-04-03 North East and Yorkshire 100
## 389 2020-04-04 North East and Yorkshire 105
## 390 2020-04-05 North East and Yorkshire 92
## 391 2020-04-06 North East and Yorkshire 96
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## 393 2020-04-08 North East and Yorkshire 107
## 394 2020-04-09 North East and Yorkshire 111
## 395 2020-04-10 North East and Yorkshire 117
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## 397 2020-04-12 North East and Yorkshire 84
## 398 2020-04-13 North East and Yorkshire 94
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## 401 2020-04-16 North East and Yorkshire 103
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## 403 2020-04-18 North East and Yorkshire 95
## 404 2020-04-19 North East and Yorkshire 88
## 405 2020-04-20 North East and Yorkshire 100
## 406 2020-04-21 North East and Yorkshire 76
## 407 2020-04-22 North East and Yorkshire 84
## 408 2020-04-23 North East and Yorkshire 63
## 409 2020-04-24 North East and Yorkshire 72
## 410 2020-04-25 North East and Yorkshire 69
## 411 2020-04-26 North East and Yorkshire 65
## 412 2020-04-27 North East and Yorkshire 65
## 413 2020-04-28 North East and Yorkshire 57
## 414 2020-04-29 North East and Yorkshire 69
## 415 2020-04-30 North East and Yorkshire 57
## 416 2020-05-01 North East and Yorkshire 64
## 417 2020-05-02 North East and Yorkshire 48
## 418 2020-05-03 North East and Yorkshire 40
## 419 2020-05-04 North East and Yorkshire 49
## 420 2020-05-05 North East and Yorkshire 40
## 421 2020-05-06 North East and Yorkshire 51
## 422 2020-05-07 North East and Yorkshire 45
## 423 2020-05-08 North East and Yorkshire 42
## 424 2020-05-09 North East and Yorkshire 44
## 425 2020-05-10 North East and Yorkshire 40
## 426 2020-05-11 North East and Yorkshire 29
## 427 2020-05-12 North East and Yorkshire 27
## 428 2020-05-13 North East and Yorkshire 28
## 429 2020-05-14 North East and Yorkshire 31
## 430 2020-05-15 North East and Yorkshire 32
## 431 2020-05-16 North East and Yorkshire 35
## 432 2020-05-17 North East and Yorkshire 26
## 433 2020-05-18 North East and Yorkshire 30
## 434 2020-05-19 North East and Yorkshire 27
## 435 2020-05-20 North East and Yorkshire 22
## 436 2020-05-21 North East and Yorkshire 33
## 437 2020-05-22 North East and Yorkshire 22
## 438 2020-05-23 North East and Yorkshire 18
## 439 2020-05-24 North East and Yorkshire 26
## 440 2020-05-25 North East and Yorkshire 21
## 441 2020-05-26 North East and Yorkshire 21
## 442 2020-05-27 North East and Yorkshire 22
## 443 2020-05-28 North East and Yorkshire 21
## 444 2020-05-29 North East and Yorkshire 25
## 445 2020-05-30 North East and Yorkshire 20
## 446 2020-05-31 North East and Yorkshire 20
## 447 2020-06-01 North East and Yorkshire 16
## 448 2020-06-02 North East and Yorkshire 23
## 449 2020-06-03 North East and Yorkshire 23
## 450 2020-06-04 North East and Yorkshire 17
## 451 2020-06-05 North East and Yorkshire 18
## 452 2020-06-06 North East and Yorkshire 21
## 453 2020-06-07 North East and Yorkshire 14
## 454 2020-06-08 North East and Yorkshire 11
## 455 2020-06-09 North East and Yorkshire 12
## 456 2020-06-10 North East and Yorkshire 18
## 457 2020-06-11 North East and Yorkshire 7
## 458 2020-06-12 North East and Yorkshire 9
## 459 2020-06-13 North East and Yorkshire 10
## 460 2020-06-14 North East and Yorkshire 11
## 461 2020-06-15 North East and Yorkshire 8
## 462 2020-06-16 North East and Yorkshire 10
## 463 2020-06-17 North East and Yorkshire 9
## 464 2020-06-18 North East and Yorkshire 10
## 465 2020-06-19 North East and Yorkshire 6
## 466 2020-06-20 North East and Yorkshire 4
## 467 2020-06-21 North East and Yorkshire 4
## 468 2020-06-22 North East and Yorkshire 6
## 469 2020-06-23 North East and Yorkshire 7
## 470 2020-06-24 North East and Yorkshire 7
## 471 2020-06-25 North East and Yorkshire 3
## 472 2020-06-26 North East and Yorkshire 1
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## 474 2020-03-02 North West 0
## 475 2020-03-03 North West 0
## 476 2020-03-04 North West 0
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## 489 2020-03-17 North West 4
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## 501 2020-03-29 North West 46
## 502 2020-03-30 North West 67
## 503 2020-03-31 North West 52
## 504 2020-04-01 North West 86
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## 506 2020-04-03 North West 95
## 507 2020-04-04 North West 98
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## 529 2020-04-26 North West 55
## 530 2020-04-27 North West 54
## 531 2020-04-28 North West 57
## 532 2020-04-29 North West 62
## 533 2020-04-30 North West 59
## 534 2020-05-01 North West 45
## 535 2020-05-02 North West 56
## 536 2020-05-03 North West 55
## 537 2020-05-04 North West 48
## 538 2020-05-05 North West 48
## 539 2020-05-06 North West 44
## 540 2020-05-07 North West 49
## 541 2020-05-08 North West 42
## 542 2020-05-09 North West 30
## 543 2020-05-10 North West 41
## 544 2020-05-11 North West 35
## 545 2020-05-12 North West 38
## 546 2020-05-13 North West 25
## 547 2020-05-14 North West 26
## 548 2020-05-15 North West 33
## 549 2020-05-16 North West 32
## 550 2020-05-17 North West 24
## 551 2020-05-18 North West 31
## 552 2020-05-19 North West 35
## 553 2020-05-20 North West 27
## 554 2020-05-21 North West 27
## 555 2020-05-22 North West 26
## 556 2020-05-23 North West 31
## 557 2020-05-24 North West 26
## 558 2020-05-25 North West 31
## 559 2020-05-26 North West 27
## 560 2020-05-27 North West 27
## 561 2020-05-28 North West 28
## 562 2020-05-29 North West 20
## 563 2020-05-30 North West 19
## 564 2020-05-31 North West 13
## 565 2020-06-01 North West 12
## 566 2020-06-02 North West 27
## 567 2020-06-03 North West 22
## 568 2020-06-04 North West 22
## 569 2020-06-05 North West 16
## 570 2020-06-06 North West 26
## 571 2020-06-07 North West 20
## 572 2020-06-08 North West 20
## 573 2020-06-09 North West 16
## 574 2020-06-10 North West 16
## 575 2020-06-11 North West 16
## 576 2020-06-12 North West 11
## 577 2020-06-13 North West 9
## 578 2020-06-14 North West 15
## 579 2020-06-15 North West 15
## 580 2020-06-16 North West 13
## 581 2020-06-17 North West 10
## 582 2020-06-18 North West 13
## 583 2020-06-19 North West 7
## 584 2020-06-20 North West 11
## 585 2020-06-21 North West 6
## 586 2020-06-22 North West 10
## 587 2020-06-23 North West 13
## 588 2020-06-24 North West 13
## 589 2020-06-25 North West 11
## 590 2020-06-26 North West 2
## 591 2020-03-01 South East 0
## 592 2020-03-02 South East 0
## 593 2020-03-03 South East 1
## 594 2020-03-04 South East 0
## 595 2020-03-05 South East 1
## 596 2020-03-06 South East 0
## 597 2020-03-07 South East 0
## 598 2020-03-08 South East 1
## 599 2020-03-09 South East 1
## 600 2020-03-10 South East 1
## 601 2020-03-11 South East 1
## 602 2020-03-12 South East 0
## 603 2020-03-13 South East 1
## 604 2020-03-14 South East 1
## 605 2020-03-15 South East 5
## 606 2020-03-16 South East 8
## 607 2020-03-17 South East 7
## 608 2020-03-18 South East 10
## 609 2020-03-19 South East 9
## 610 2020-03-20 South East 13
## 611 2020-03-21 South East 7
## 612 2020-03-22 South East 25
## 613 2020-03-23 South East 20
## 614 2020-03-24 South East 22
## 615 2020-03-25 South East 29
## 616 2020-03-26 South East 35
## 617 2020-03-27 South East 34
## 618 2020-03-28 South East 36
## 619 2020-03-29 South East 55
## 620 2020-03-30 South East 58
## 621 2020-03-31 South East 65
## 622 2020-04-01 South East 66
## 623 2020-04-02 South East 55
## 624 2020-04-03 South East 72
## 625 2020-04-04 South East 80
## 626 2020-04-05 South East 82
## 627 2020-04-06 South East 88
## 628 2020-04-07 South East 100
## 629 2020-04-08 South East 83
## 630 2020-04-09 South East 104
## 631 2020-04-10 South East 88
## 632 2020-04-11 South East 88
## 633 2020-04-12 South East 88
## 634 2020-04-13 South East 84
## 635 2020-04-14 South East 65
## 636 2020-04-15 South East 72
## 637 2020-04-16 South East 56
## 638 2020-04-17 South East 86
## 639 2020-04-18 South East 57
## 640 2020-04-19 South East 70
## 641 2020-04-20 South East 87
## 642 2020-04-21 South East 51
## 643 2020-04-22 South East 54
## 644 2020-04-23 South East 57
## 645 2020-04-24 South East 64
## 646 2020-04-25 South East 51
## 647 2020-04-26 South East 51
## 648 2020-04-27 South East 40
## 649 2020-04-28 South East 40
## 650 2020-04-29 South East 47
## 651 2020-04-30 South East 29
## 652 2020-05-01 South East 37
## 653 2020-05-02 South East 36
## 654 2020-05-03 South East 17
## 655 2020-05-04 South East 35
## 656 2020-05-05 South East 29
## 657 2020-05-06 South East 25
## 658 2020-05-07 South East 27
## 659 2020-05-08 South East 26
## 660 2020-05-09 South East 28
## 661 2020-05-10 South East 19
## 662 2020-05-11 South East 25
## 663 2020-05-12 South East 27
## 664 2020-05-13 South East 18
## 665 2020-05-14 South East 32
## 666 2020-05-15 South East 24
## 667 2020-05-16 South East 22
## 668 2020-05-17 South East 18
## 669 2020-05-18 South East 22
## 670 2020-05-19 South East 12
## 671 2020-05-20 South East 22
## 672 2020-05-21 South East 15
## 673 2020-05-22 South East 17
## 674 2020-05-23 South East 21
## 675 2020-05-24 South East 17
## 676 2020-05-25 South East 13
## 677 2020-05-26 South East 19
## 678 2020-05-27 South East 18
## 679 2020-05-28 South East 12
## 680 2020-05-29 South East 21
## 681 2020-05-30 South East 8
## 682 2020-05-31 South East 12
## 683 2020-06-01 South East 11
## 684 2020-06-02 South East 13
## 685 2020-06-03 South East 17
## 686 2020-06-04 South East 11
## 687 2020-06-05 South East 11
## 688 2020-06-06 South East 10
## 689 2020-06-07 South East 12
## 690 2020-06-08 South East 8
## 691 2020-06-09 South East 10
## 692 2020-06-10 South East 11
## 693 2020-06-11 South East 5
## 694 2020-06-12 South East 6
## 695 2020-06-13 South East 6
## 696 2020-06-14 South East 7
## 697 2020-06-15 South East 7
## 698 2020-06-16 South East 11
## 699 2020-06-17 South East 8
## 700 2020-06-18 South East 4
## 701 2020-06-19 South East 6
## 702 2020-06-20 South East 5
## 703 2020-06-21 South East 3
## 704 2020-06-22 South East 2
## 705 2020-06-23 South East 8
## 706 2020-06-24 South East 6
## 707 2020-06-25 South East 4
## 708 2020-06-26 South East 2
## 709 2020-03-01 South West 0
## 710 2020-03-02 South West 0
## 711 2020-03-03 South West 0
## 712 2020-03-04 South West 0
## 713 2020-03-05 South West 0
## 714 2020-03-06 South West 0
## 715 2020-03-07 South West 0
## 716 2020-03-08 South West 0
## 717 2020-03-09 South West 0
## 718 2020-03-10 South West 0
## 719 2020-03-11 South West 1
## 720 2020-03-12 South West 0
## 721 2020-03-13 South West 0
## 722 2020-03-14 South West 1
## 723 2020-03-15 South West 0
## 724 2020-03-16 South West 0
## 725 2020-03-17 South West 2
## 726 2020-03-18 South West 2
## 727 2020-03-19 South West 4
## 728 2020-03-20 South West 3
## 729 2020-03-21 South West 6
## 730 2020-03-22 South West 7
## 731 2020-03-23 South West 8
## 732 2020-03-24 South West 7
## 733 2020-03-25 South West 9
## 734 2020-03-26 South West 11
## 735 2020-03-27 South West 13
## 736 2020-03-28 South West 21
## 737 2020-03-29 South West 18
## 738 2020-03-30 South West 23
## 739 2020-03-31 South West 23
## 740 2020-04-01 South West 22
## 741 2020-04-02 South West 23
## 742 2020-04-03 South West 30
## 743 2020-04-04 South West 42
## 744 2020-04-05 South West 32
## 745 2020-04-06 South West 34
## 746 2020-04-07 South West 39
## 747 2020-04-08 South West 47
## 748 2020-04-09 South West 24
## 749 2020-04-10 South West 46
## 750 2020-04-11 South West 43
## 751 2020-04-12 South West 23
## 752 2020-04-13 South West 27
## 753 2020-04-14 South West 24
## 754 2020-04-15 South West 32
## 755 2020-04-16 South West 29
## 756 2020-04-17 South West 33
## 757 2020-04-18 South West 25
## 758 2020-04-19 South West 31
## 759 2020-04-20 South West 26
## 760 2020-04-21 South West 26
## 761 2020-04-22 South West 23
## 762 2020-04-23 South West 17
## 763 2020-04-24 South West 19
## 764 2020-04-25 South West 15
## 765 2020-04-26 South West 27
## 766 2020-04-27 South West 13
## 767 2020-04-28 South West 17
## 768 2020-04-29 South West 15
## 769 2020-04-30 South West 26
## 770 2020-05-01 South West 6
## 771 2020-05-02 South West 7
## 772 2020-05-03 South West 10
## 773 2020-05-04 South West 17
## 774 2020-05-05 South West 14
## 775 2020-05-06 South West 19
## 776 2020-05-07 South West 16
## 777 2020-05-08 South West 6
## 778 2020-05-09 South West 11
## 779 2020-05-10 South West 5
## 780 2020-05-11 South West 8
## 781 2020-05-12 South West 7
## 782 2020-05-13 South West 7
## 783 2020-05-14 South West 6
## 784 2020-05-15 South West 4
## 785 2020-05-16 South West 4
## 786 2020-05-17 South West 6
## 787 2020-05-18 South West 4
## 788 2020-05-19 South West 6
## 789 2020-05-20 South West 1
## 790 2020-05-21 South West 9
## 791 2020-05-22 South West 6
## 792 2020-05-23 South West 6
## 793 2020-05-24 South West 3
## 794 2020-05-25 South West 8
## 795 2020-05-26 South West 11
## 796 2020-05-27 South West 5
## 797 2020-05-28 South West 10
## 798 2020-05-29 South West 7
## 799 2020-05-30 South West 3
## 800 2020-05-31 South West 2
## 801 2020-06-01 South West 7
## 802 2020-06-02 South West 2
## 803 2020-06-03 South West 7
## 804 2020-06-04 South West 2
## 805 2020-06-05 South West 2
## 806 2020-06-06 South West 1
## 807 2020-06-07 South West 3
## 808 2020-06-08 South West 3
## 809 2020-06-09 South West 0
## 810 2020-06-10 South West 1
## 811 2020-06-11 South West 2
## 812 2020-06-12 South West 2
## 813 2020-06-13 South West 2
## 814 2020-06-14 South West 0
## 815 2020-06-15 South West 1
## 816 2020-06-16 South West 2
## 817 2020-06-17 South West 0
## 818 2020-06-18 South West 0
## 819 2020-06-19 South West 0
## 820 2020-06-20 South West 2
## 821 2020-06-21 South West 0
## 822 2020-06-22 South West 1
## 823 2020-06-23 South West 1
## 824 2020-06-24 South West 1
## 825 2020-06-25 South West 0
## 826 2020-06-26 South West 11.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 25 Jun 2020.
1.6 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.4786 -3.2020 -0.3888 3.6233 5.9215
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.848e+00 5.477e-02 88.52 <2e-16 ***
## note_lag 1.245e-05 5.581e-07 22.30 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 13.83783)
##
## Null deviance: 7335.7 on 56 degrees of freedom
## Residual deviance: 786.7 on 55 degrees of freedom
## (23 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 127.474937 1.000012
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 114.352477 141.739949
## note_lag 1.000011 1.000014
Rsq(lag_mod)
## [1] 0.8927576
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.4.0 lubridate_1.7.9
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.14
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.5.0 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_4.0.2 knitr_1.29
## [13] jsonlite_1.7.0 broom_0.5.6 dbplyr_1.4.4 compiler_4.0.2
## [17] httr_1.4.1 backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.5.0 prettyunits_1.1.1 tools_4.0.2
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.1 nlme_3.1-148 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.15 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.6.0 MASS_7.3-51.6 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-25
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-41 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.1 foreign_0.8-80 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-12 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.3
## [77] viridis_0.5.1 grid_4.0.2 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0